Arginine vasopressin pro-hormone as predictive biomarker for diabetes

ABSTRACT

Subject of the present invention are assays and in vitro methods for the prediction of the risk of a subject for contracting metabolic syndrome and/or diabetes mellitus and for diagnosing metabolic syndrome, comprising determining the level of arginine vasopressin pro-hormone or fragments thereof in a sample of a subject.

FIELD OF THE INVENTION

The present invention is in the field of prognostic biomarkers andprognostic assays. The application particularly relates to theprediction of diabetes and diagnosis of metabolic syndrome.

BACKGROUND OF THE INVENTION

Arginin Vasopressin (AVP), also known as antidiuretic hormone (ADH), isproduced in the hypothalamus and released from the neuropituitary glandin conditions of high plasma osmolality, low plasma volume and low bloodpressure. AVP binds to three different receptors (V1aR, V1bR and V2R).The V1aR is widely expressed (Nature 1993:356:523-526), whereas V1bR andV2R are more specifically expressed in the pituitary gland and kidneycollecting ducts, respectively (Nature 1992:336-339 and FEBS Lett1994:356:215-220). The antidiuretic effect of AVP is mediated throughV2R and pharmacological blockade of V2R has favourable effects in thetreatment of hyponatremia and heart failure whereas the prothromboticand vasoconstrictor effects of AVP, which are used clinically inbleeding and hypotensive disorders are primarily mediated through theV1aR. In addition, AVP action has suggested to be linked to modulationof adrenocorticotropic hormone (ACTH) release (V1bR), stimulation ofliver glycogenolysis (V1 aR) and stimulation of insulin and glucagonsecretion (V1aR) (Morel et al., Nature 1992; 356(6369):523-6; de Keyzeret al., FEBS letters 1994; 356(2-3):215-20; Ventura et al., Journal ofmolecular endocrinology 1999; 22(3):251-60).

Previous studies on humans and animal models have indicated a role ofthe AVP system in glucose homeostasis, insulin resistance and diabetesmellitus. In patients with poorly controlled diabetes mellitus andincreased osmolality, plasma AVP is markedly elevated (Lolait et al.,Nature 1992; 357(6376):336-9) and in healthy subjects AVP infusion leadsto increased blood glucose levels (Schrier et al., N Engl J Med 2006;355(20):2099-112). Mice lacking the V1aR display impaired glucosetolerance, insulin resistance and elevated AVP levels (Gheorghiade etal., Jama 2007; 297(12):1332-43; Federici et al., Annals of medicine2007; 39(5):346-58) while mice who lack the V1bR has the oppositephenotype of lower fasting plasma glucose and increased in insulinsensitivity, in comparison with normal mice (Dunser et al., Circulation2003; 107(18):2313-9).

The vasopressin gene encodes a precursor protein (pre-pro-AVP)comprising a 19 amino acid signal sequence, arginine vasopressin and twoassociated proteins, neurophysin II and a glycopeptide, copeptin.Copeptin is a biologically inactive cleavage product of the C-terminusof the AVP precursor and is produced in equimolar amounts with AVP.However, in contrast to AVP, copeptin is stable, has a long half-life,is not bound to platelets and therefore found in considerably higherconcentrations in plasma than AVP, and it has been proposed as analternative diagnostic target to assess vasopressin release (Struck J,Morgenthaler N G, Bergmann A. Copeptin, a stable peptide derived fromthe vasopressin precursor, is elevated in serum of sepsis patients.Peptides. 2005 December; 26(12):2500-4.).

AVP has been described as diagnostic marker for diabetes mellitus and itwas known that AVP level is related with diabetes mellitus as an acuteor chronic stressor using type I diabetes mellitus animal models. (SunShin Yi et al., Enhanced expressions of arginine vasopressing (Avp) inthe hypothalamic paraventricular and supraoptic nucleic of type 2diabetic rats, Neurochem Res (2008) 33:833-841)

SUMMARY OF THE INVENTION

It was surprisingly found by the present inventors that there is arelation between relatively increased levels of Arginine Vasopressin(AVP) pro-hormone or fragments thereof and the presence of metabolicsyndrome and the risk of developing diabetes. In this context asensitive and precise Copeptin assay comprising at least one monoclonalantibody is provided by the present invention.

Subject of the present invention is thus an in vitro method forpredicting the risk of a subject for contracting diabetes mellitusand/or metabolic syndrome or for identifying a subject having anenhanced risk for contracting diabetes mellitus and/or metabolicsyndrome or for diagnosing metabolic syndrome in a subject, comprisingthe following steps:

-   -   a. providing a sample from said subject,    -   b. determining the level of arginine vasopressin pro-hormone or        fragments thereof in said sample,    -   c. using said level of arginine vasopressin pro-hormone or        fragments thereof for the prediction of the probability of the        subject to contract diabetes mellitus and/or metabolic syndrome        or for inferring from it a risk for contracting diabetes        mellitus and/or metabolic syndrome for said subject or for        diagnosing metabolic syndrome in said subject.

A further subject of the present invention is a sensitive and precisecopeptin assay.

DETAILED DESCRIPTION OF THE INVENTION

The present invention relates to a method for predicting the risk of asubject for contracting diabetes mellitus and/or metabolic syndrome orfor identifying a subject having an enhanced risk for contractingdiabetes mellitus and/or metabolic syndrome or for diagnosing metabolicsyndrome in a subject, comprising the following steps:

-   -   a. providing a sample from said subject,    -   b. determining the level of arginine vasopressin pro-hormone or        fragments thereof in said sample,    -   c. using said level of arginine vasopressin pro-hormone or        fragments thereof for the prediction of the probability of the        subject to contract diabetes mellitus and/or metabolic syndrome        or for inferring from it a risk for contracting diabetes        mellitus and/or metabolic syndrome for said subject or for        diagnosing metabolic syndrome in said subject.

Herein, “using said level of arginine vasopressin pro-hormone orfragments thereof for the prediction of the probability of the subjectto contract diabetes mellitus and/or metabolic syndrome or for inferringfrom it a risk for contracting diabetes mellitus and/or metabolicsyndrome for said subject or for diagnosing metabolic syndrome in saidsubject” may have the meaning of “correlating said level of argininevasopressin pro-hormone or fragments thereof with the risk of thesubject to contract diabetes mellitus and/or metabolic syndrome or withthe diagnosis of metabolic syndrome in said subject”.

In a preferred embodiment for all aspects of the invention said subjectis apparently healthy.

“Apparently healthy”, as used herein, relates to individuals who do notsuffer from diabetes mellitus. In some embodiments, the apparentlyhealthy subjects are suffering from metabolic syndrome. The inventivemethod may be used for diagnosing whether a subject has metabolicsyndrome or not.

The term “subject” as used herein refers to a living human or non-humanorganism. Preferably herein the subject is a human subject.

Preferably, said subject is non-diabetic.

In a very particular embodiment the apparently healthy subjects areindividuals who have not previously had or are not aware of having had acardiovascular or a coronary event or heart failure or are not sufferingfrom a cardiovascular disease. Coronary events are defined as fatal ornon-fatal acute coronary syndromes including myocardial infarction, ordeath due to ischemic heart disease. Cardiovascular events are definedas fatal or non-fatal acute coronary syndromes including myocardialinfarction, fatal or non-fatal stroke, or death due to cardiovasculardisease. Additionally, the apparently healthy subjects may in a veryparticular embodiment not have an acute infectious disease.

In another very particular embodiment, the apparently healthy subjectsmay be suffering from a cardiovascular disease and/or may have had acardiovascular or a coronary event or heart failure. In certainembodiments they may be suffering from an infectious disease.

In one embodiment the subject exhibits impaired fasting glycaemia(impaired fasting glucose), i.e. a fasting glucose level in the blood(fasting blood glucose (FBG) or fasting blood sugar (FBS)) of less than6.1 but more than 5.4 mmol/L. Thus, in an especially preferredembodiment said subject is a non-diabetic with fasting blood glucose ofless than 6.1 mmol/L but more than 5.4 mmol/L.

In another especially preferred embodiment said subject is a subjectwithout impaired fasting glycaemia. Thus, in a especially preferredembodiment said subject is a subject with fasting blood glucose of lessthan 5.4 mmol/L.

Fasting blood glucose is measured at least 8 hours after the last foodintake of the subject, typically 8 to 10 hours after the last foodintake.

(Fasting) blood glucose levels herein are given in terms of whole bloodglucose levels. In general, glucose can be measured in whole blood,serum or plasma, but reference values differ depending on the type ofsample.

Determining (or measuring or detecting) the level of argininevasopressin pro-hormone or fragments thereof herein is performed using adiagnostic assay as explained below.

A preferred fragment of arginine vasopressin pro-hormone (pro-AVP) isC-terminal pro-AVP (CT-pro-AVP or Copeptin), i.e. a posttranslationallymodified (glycosylated) peptide covering amino acid positions 107-145 ofpro-AVP (126-164 of pre-pro-AVP).

In a preferred embodiment the fragment of arginine vasopressinpro-hormone (pro-AVP) is not the mature AVP.

In a particularly preferred embodiment of the method, the level ofcopeptin is determined.

As mentioned herein in the context of pro-hormones and other peptides,the term “fragment” refers to smaller proteins or peptides derivablefrom larger proteins or peptides, which hence comprise a partialsequence of the larger protein or peptide. Said fragments are derivablefrom the larger proteins or peptides by saponification of one or more ofits peptide bonds.

“Fragments” of arginine vasopressin pro-hormone (pro-AVP) preferablyrelate to fragments of at least 6 amino acids in length, most preferablyat least 12 amino acid residues in length. Such fragments are preferablydetectable with immunological assays as described herein.

In the context of the present invention, the term “level” in expressionssuch as “level of a pro-hormone” and similar expressions, refers to thequantity of the molecular entity mentioned in the respective context, orin the case of enzymes it can also refer to the enzyme activity.

The methods according to the present invention are especially wellsuited for the prediction of the risk of developing diabetes type II.

The prediction of the risk of the subject for contracting diabetesmellitus and/or metabolic syndrome or the diagnosis of metabolicsyndrome may in one embodiment be improved by additionally determiningand using the level of at least one laboratory parameter or furthermarker selected from the group comprising fasting blood or plasmaglucose, triglycerides, density lipoprotein (HDL) cholesterol orsubfractions thereof, low-density lipoprotein (LDL) cholesterol orsubfractions thereof, Cystatin C, Insulin, C-reactive protein (CRP),natriuretic peptides of the A- and the B-type as well as theirprecursors and fragments thereof including type A natriuretic peptide(ANP), proANP, N-terminal-proANP (NT-proANP), midregional-proANP(MR-proANP), type B natriuretic peptide (BNP), proBNP, N-terminal-proBNP(NT-proBNP), growth differentiation factor 15 (GDF15), interleukin 1receptor-like 1 (ST2), procalcitonin and fragments thereof,pro-Adrenomedullin and fragments thereof including adreneomedullin(ADM), Proadrenomedullin N-terminal 20 peptide (PAMP),mid-regional-proADM (MR-proADM), C-terminal proADM (CT-proADM)pro-Endothelin-1 and fragments thereof including C-terminal-proET-1(CT-proET-1), N-terminal-proET-1 (NT-proET-1), big-Endothelin-1 andEndothelin-1.

In particular embodiments of the invention, additionally at least oneclinical parameter is determined selected from the group comprising age,gender, systolic blood pressure, diastolic blood pressure,antihypertensive treatment (AHT), body mass index, waist circumference,waist-hip-ratio, current smoker, diabetes heredity and previouscardiovascular disease (CVD).

According to the World Health Organization (WHO), diabetes is a chronicdisease, which occurs when the pancreas does not produce enough insulin,or when the body cannot effectively use the insulin it produces. Thisleads to an increased concentration of glucose in the blood(hyperglycaemia). Type 1 diabetes (previously known as insulin-dependentor childhood-onset diabetes) is characterized by a lack of insulinproduction. Type 2 diabetes (formerly called non-insulin-dependent oradult-onset diabetes) is caused by the body's ineffective use ofinsulin. It often results from excess body weight and physicalinactivity.

Diabetes mellitus type 2 or Type 2 diabetes is a metabolic disorder thatis primarily characterized by insulin resistance, relative insulindeficiency, and hyperglycaemia. The World Health Organization definitionof diabetes is for a single raised glucose reading with symptoms,otherwise raised values on two occasions, of either: (i) fasting plasmaglucose≧7.0 mmol/l (126 mg/dl) or (ii) with a Glucose tolerance test(GTT), two hours after the oral dose a plasma glucose≧11.1 mmol/l (200mg/dl). A common initial symptom of type 2 diabetes is a faint smell offruit or vegetable odour on the breath. This is caused by ketosisinduced by lack of response to insulin, leading to lack of glucoseinternal to cells. It commonly accompanies high blood glucose levels,which are also a result of decreased sensitivity to insulin.

Metabolic syndrome is a combination of medical disorders that increasethe risk of developing cardiovascular disease and diabetes. It affects agreat number of people, and prevalence increases with age. Some studiesestimate the prevalence in the USA to be up to 25% of the population.

Metabolic syndrome is also known as metabolic syndrome X, syndrome X,insulin resistance syndrome, Reaven's syndrome, and CHAOS (Australia).It is important to identify those individuals with metabolic syndromeearly, so that lifestyle interventions and treatment may prevent thedevelopment of diabetes and/or cardiovascular disease. The underlyingcause of the metabolic syndrome continues to challenge the experts. Bothinsulin resistance and central obesity are considered significantfactors. Genetics, physical inactivity, ageing, a proinflammatory stateand hormonal changes may also have a causal effect, but the role ofthese may vary depending on ethnic group. As a consequence of theuncertainty about the mechanistic cause of the metabolic syndrome a lotof confusion has existed about the definition of the metabolic syndrome.The existence of multiple definitions for the metabolic syndrome hascaused confusion and has resulted in many studies and research paperscomparing the merits of each definition. According to the ExecutiveSummary of The Third Report of The National Cholesterol EducationProgram (NCEP) Expert Panel on Detection, Evaluation, And Treatment ofHigh Blood Cholesterol In Adults (Adult Treatment Panel III) (JAMA.2001; 285(19):2486-2497) metabolic syndrome is defined as the presenceof three or more of the following components as follows: (1) Waistcircumference≧88 cm (35 inches) in women or ≧102 cm (40 inches) in men,(2) BP≧130/85 mmHg or treatment for hypertension, (3) fastingtriglycerides≧150 mg/dL, (4) HDL cholesterol≦40 mg/dL in men or ≦50mg/dL in women, and (5) fasting blood glucose≧110 mg/dL or treatment fordiabetes. A more recent definition with some modifications has beengiven by the International Diabetes Federation(idf.org/webdata/docs/IDF_Meta_def_final.pdf). As the goal of definingthe metabolic syndrome is the identification of subjects at increasedrisk for developing diabetes and/or cardiovascular diseases, prognosticfactors independently associated with such risk can be directlyassociated with the metabolic syndrome and in that sense are diagnosticfor the presence of the metabolic syndrome. Accordingly, a marker, whichis independently predictive for the development of diabetes, as is shownfor copeptin in the present invention, has to be considered at the sametime a diagnostic marker (along with other diagnostic laboratory andclinical parameters) for the presence of the metabolic syndrome, and itcan even be anticipated that it will become part of the definition ofthe metabolic syndrome in the future.

Thus, the determined levels of arginine vasopressin pro-hormone orfragments thereof in a samples of subjects may be used in conjunctionwith other clinical and/or laboratory parameters to help to define“metabolic syndrome” as a medical condition. In other words, the levelof arginine vasopressin pro-hormone or fragments thereof may be used asadditional parameter to improve the definition of metabolic syndrome interms of decisive criteria since the current criteria as outlined abovedo not in all cases lead to satisfactory results.

Insulin resistance (IR) is a state in which a given concentration ofinsulin produces a less-than-expected biological effect. Insulinresistance has also been arbitrarily defined as the requirement of 200or more units of insulin per day to attain glycemic control and toprevent ketosis. High plasma levels of insulin and glucose due toinsulin resistance often lead to metabolic syndrome and type 2 diabetes,including its complications. Symptoms of IR may comprise fatigue, brainfogginess, inability to focus, low blood sugar, intestinal bloating,sleepiness, weight gain, fat storage, difficulty losing weight,increased blood triglyceride levels, increased blood pressure, anddepression.

The invention also pertains to a method for predicting the risk of asubject for contracting diabetes mellitus and/or metabolic syndrome orfor identifying a subject having an enhanced risk for contractingdiabetes mellitus and/or metabolic syndrome or for diagnosing metabolicsyndrome in a subject as described above, wherein the level of argininevasopressin pro-hormone or fragments thereof either alone or inconjunction with other prognostically useful laboratory or clinicalparameters is used for the prediction of the risk of a subject forcontracting diabetes mellitus and/or metabolic syndrome or for thediagnosis of metabolic syndrome by a method which may be selected fromthe following alternatives:

-   -   Comparison with the median of the level of arginine vasopressin        pro-hormone or fragments thereof in an ensemble of        pre-determined samples in a population of apparently healthy        subjects,    -   Comparison with a quantile of the level of arginine vasopressin        pro-hormone or fragments thereof in an ensemble of        pre-determined samples in a population of apparently healthy        subjects,    -   Calculation based on Cox Proportional Hazards analysis or by        using Risk index calculations such as the NRI (Net        Reclassification Index) or the IDI (Integrated Discrimination        Index).

In a preferred embodiment the level of arginine vasopressin (AVP)pro-hormone or fragments thereof having at least a length of 12 aminoacids is determined in a diagnostic assay, preferably by an immunoassay.It is particularly preferred that the level of copeptin or fragmentsthereof having at least a length of 12 amino acids is determined.

The invention may also involve comparing the level of a marker (herearginine vasopressin pro-hormone or fragments thereof such as copeptin)for the individual with a predetermined value. The predetermined valuecan take a variety of forms. It can be single cut-off value, such as forinstance a median or mean or the 75th, 90th, 95th or 99th percentile ofa population. It can be established based upon comparative groups, suchas where the risk in one defined group is double the risk in anotherdefined group. It can be a range, for example, where the testedpopulation is divided equally (or unequally) into groups, such as alow-risk group, a medium-risk group and a high-risk group, or intoquartiles, the lowest quartile being individuals with the lowest riskand the highest quartile being individuals with the highest risk.

The predetermined value can vary among particular populations selected,depending on their habits, ethnicity, genetics etc. For example, anapparently healthy, non-smoker population (no detectable disease,particularly no diabetes mellitus) might have a different ‘normal’ rangeof markers than a smoking population or a population the members ofwhich have diabetes mellitus.

Accordingly, the predetermined values selected may take into account thecategory in which an individual falls. Appropriate ranges and categoriescan be selected with no more than routine experimentation by those ofordinary skill in the art.

In a specific embodiment of the method according to the invention theuse of said level of arginine vasopressin pro-hormone or fragmentsthereof comprises comparing said level of arginine vasopressinpro-hormone or fragments thereof to a threshold level, whereby, whensaid level of arginine vasopressin pro-hormone or fragments thereofexceeds said threshold level, diabetes mellitus and/or metabolicsyndrome is predicted in a subject or a subject having an enhanced riskfor getting diabetes mellitus and/or metabolic syndrome is identified.

Other preferred cut-off values are for instance the 90th, 95th or 99thpercentile of a normal population. By using a higher percentile than the75th percentile, one reduces the number of false positive subjectsidentified, but one might miss to identify subjects, who are atmoderate, albeit still increased risk. Thus, one might adopt the cut-offvalue depending on whether it is considered more appropriate to identifymost of the subjects at risk at the expense of also identifying “falsepositives”, or whether it is considered more appropriate to identifymainly the subjects at high risk at the expense of missing severalsubjects at moderate risk.

Other mathematical possibilities to calculate an individual's risk byusing the individual's copeptin value and other prognostic laboratoryand clinical parameters are for instance based on Cox regressionanalysis or are the NRI (Net Reclassification Index) or the IDI(Integrated Discrimination Index). The indices can be calculatedaccording to Pencina (Pencina M J, et al.: Evaluating the addedpredictive ability of a new marker: from area under the ROC curve toreclassification and beyond. Stat Med. 2008; 27:157-172).

For instance a copeptin level of 5.04 pmol/L (i.e. the medianconcentration of a reference population of apparently healthy subjects)or above, preferably above 7.93 pmol/L (i.e. the Q3/Q4 borderconcentration of a reference population of apparently healthy subjects)in the sample may be indicative for an elevated risk of the patient tocontract diabetes mellitus. Q1, Q2, Q3 and Q4 herein refer to quartilesof a reference population of apparently healthy subjects.

In a further particular aspect the invention pertains to a method fordiagnosing metabolic syndrome in a subject comprising the followingsteps:

-   -   a. providing a sample from said subject,    -   b. determining the level of arginine vasopressin pro-hormone or        fragments thereof in said sample    -   c. determining other clinical and/or laboratory parameters        associated with the diagnosis of metabolic syndrome    -   d. correlating the level of arginine vasopressin pro-hormone or        fragments thereof in conjunction with other clinical and/or        laboratory parameters associated with the diagnosis of metabolic        syndrome.

Also within the scope of the present invention is an assay for thedetermination of the copeptin level in a sample for predicting the riskof a subject to contract diabetes mellitus and/or metabolic syndrome ordiagnosing metabolic syndrome.

Preferably, the assay has a total assay imprecision of 20% coefficientof variation (CV) at a concentration below 2.2 pmol/L and a lower than20% CV at normal range-concentrations above that concentration, and/or10% CV at a concentration below 9 pmol/l and a lower than 10% CV atnormal range-concentrations above that concentration.

In another preferred embodiment, the assay has an total assayimprecision of maximally 12% at the median Copeptin concentrationdetermined in serum or plasma of a Caucasian population of healthyfasting females at rest.

In a preferred embodiment of the above described assays, the assaycomprises two anti-copeptin antibodies, wherein at least one of theantibodies is a monoclonal antibody or fragment or recombinant variantthereof.

The invention also relates to an assay for the determination of thecopeptin level in a sample, wherein the assay comprises twoanti-copeptin antibodies, wherein at least one of the antibodies is amonoclonal antibody or fragment or recombinant variant thereof.

Preferably, the assay comprises a monoclonal and a polyclonal antibody.

In another preferred embodiment of the assays of the invention, onemonoclonal antibody binds an epitope comprised in a peptide representingpositions 132-147 of pre-pro-vasopressin.

The levels of the markers as obtained by the methods or by the use ofthe assays according to the present invention may be analyzed in anumber of fashions well known to a person skilled in the art. Forexample, each assay result obtained may be compared to a “normal” value,or a value indicating a particular disease or outcome. A particulardiagnosis/prognosis may depend upon the comparison of each assay resultto such a value, which may be referred to as a diagnostic or prognostic“threshold”. In certain embodiments, assays for one or more diagnosticor prognostic indicators are correlated to a condition or disease bymerely the presence or absence of the indicator(s) in the assay. Forexample, an assay can be designed so that a positive signal only occursabove a particular threshold concentration of interest, and below whichconcentration the assay provides no signal above background.

The sensitivity and specificity of a diagnostic and/or prognostic testdepends on more than just the analytical “quality” of the test, theyalso depend on the definition of what constitutes an abnormal result. Inpractice, Receiver Operating Characteristic curves (ROC curves), aretypically calculated by plotting the value of a variable versus itsrelative frequency in “normal” (i.e. apparently healthy) and “disease”populations (i.e. patients suffering from diabetes, insulin resistanceand/or metabolic syndrome). For any particular marker, a distribution ofmarker levels for subjects with and without a disease will likelyoverlap. Under such conditions, a test does not absolutely distinguishnormal from disease with 100% accuracy, and the area of overlapindicates where the test cannot distinguish normal from disease. Athreshold is selected, above which (or below which, depending on how amarker changes with the disease) the test is considered to be abnormaland below which the test is considered to be normal. The area under theROC curve is a measure of the probability that the perceived measurementwill allow correct identification of a condition. ROC curves can be usedeven when test results don't necessarily give an accurate number. Aslong as one can rank results, one can create a ROC curve. For example,results of a test on “disease” samples might be ranked according todegree (e.g. 1=low, 2=normal, and 3=high). This ranking can becorrelated to results in the “normal” population, and a ROC curvecreated. These methods are well known in the art. See, e.g., Hanley etal., Radiology 143: 29-36 (1982). Preferably, a threshold is selected toprovide a ROC curve area of greater than about 0.5, more preferablygreater than about 0.7, still more preferably greater than about 0.8,even more preferably greater than about 0.85, and most preferablygreater than about 0.9. The term “about” in this context refers to +/−5%of a given measurement.

The horizontal axis of the ROC curve represents (1-specificity), whichincreases with the rate of false positives. The vertical axis of thecurve represents sensitivity, which increases with the rate of truepositives. Thus, for a particular cut-off selected, the value of(1-specificity) may be determined, and a corresponding sensitivity maybe obtained. The area under the ROC curve is a measure of theprobability that the measured marker level will allow correctidentification of a disease or condition. Thus, the area under the ROCcurve can be used to determine the effectiveness of the test.

In certain embodiments, particular thresholds for one or more markers ina panel are not relied upon to determine if a profile of marker levelsobtained from a subject are indicative of a particulardiagnosis/prognosis. Rather, the present invention may utilize anevaluation of a marker panel “profile” as a unitary whole. A particular“fingerprint” pattern of changes in such a panel of markers may, ineffect, act as a specific diagnostic or prognostic indicator. Asdiscussed herein, that pattern of changes may be obtained from a singlesample, or from temporal changes in one or more members of the panel (ora panel response value). A panel herein refers to a set of markers.

As described herein after, a panel response value is preferablydetermined by plotting ROC curves for the sensitivity of a particularpanel of markers versus 1-(specificity) for the panel at variouscut-offs. In these methods, a profile of marker measurements from asubject is considered together to provide a global probability(expressed either as a numeric score or as a percentage risk) of adiagnosis or prognosis. In such embodiments, an increase in a certainsubset of markers may be sufficient to indicate a particulardiagnosis/prognosis in one patient, while an increase in a differentsubset of markers may be sufficient to indicate the same or a differentdiagnosis/prognosis in another patient. Weighting factors may also beapplied to one or more markers in a panel, for example, when a marker isof particularly high utility in identifying a particulardiagnosis/prognosis, it may be weighted so that at a given level italone is sufficient to signal a positive result. Likewise, a weightingfactor may provide that no given level of a particular marker issufficient to signal a positive result, but only signals a result whenanother marker also contributes to the analysis.

In certain embodiments, markers and/or marker panels are selected toexhibit at least about 70% sensitivity, more preferably at least about80% sensitivity, even more preferably at least about 85% sensitivity,still more preferably at least about 90% sensitivity, and mostpreferably at least about 95% sensitivity, combined with at least about70% specificity, more preferably at least about 80% specificity, evenmore preferably at least about 85% specificity, still more preferably atleast about 90% specificity, and most preferably at least about 95%specificity. In particularly preferred embodiments, both the sensitivityand specificity are at least about 75%, more preferably at least about80%, even more preferably at least about 85%, still more preferably atleast about 90%, and most preferably at least about 95%. The term“about” in this context refers to +/−5% of a given measurement.

In other embodiments, a positive likelihood ratio, negative likelihoodratio, odds ratio, or hazard ratio is used as a measure of a test'sability to predict risk or diagnose a disease. In the case of a positivelikelihood ratio, a value of 1 indicates that a positive result isequally likely among subjects in both the “diseased” and “control”groups; a value greater than 1 indicates that a positive result is morelikely in the diseased group; and a value less than 1 indicates that apositive result is more likely in the control group. In the case of anegative likelihood ratio, a value of 1 indicates that a negative resultis equally likely among subjects in both the “diseased” and “control”groups; a value greater than 1 indicates that a negative result is morelikely in the test group; and a value less than 1 indicates that anegative result is more likely in the control group. In certainpreferred embodiments, markers and/or marker panels are preferablyselected to exhibit a positive or negative likelihood ratio of at leastabout 1.5 or more or about 0.67 or less, more preferably at least about2 or more or about 0.5 or less, still more preferably at least about 5or more or about 0.2 or less, even more preferably at least about 10 ormore or about 0.1 or less, and most preferably at least about 20 or moreor about 0.05 or less. The term “about” in this context refers to +/−5%of a given measurement.

In the case of an odds ratio, a value of 1 indicates that a positiveresult is equally likely among subjects in both the “diseased” and“control” groups; a value greater than 1 indicates that a positiveresult is more likely in the diseased group; and a value less than 1indicates that a positive result is more likely in the control group. Incertain preferred embodiments, markers and/or marker panels arepreferably selected to exhibit an odds ratio of at least about 2 or moreor about 0.5 or less, more preferably at least about 3 or more or about0.33 or less, still more preferably at least about 4 or more or about0.25 or less, even more preferably at least about 5 or more or about 0.2or less, and most preferably at least about 10 or more or about 0.1 orless. The term “about” in this context refers to +/−5% of a givenmeasurement.

In the case of a hazard ratio, a value of 1 indicates that the relativerisk of an endpoint (e.g., death) is equal in both the “diseased” and“control” groups; a value greater than 1 indicates that the risk isgreater in the diseased group; and a value less than 1 indicates thatthe risk is greater in the control group. In certain preferredembodiments, markers and/or marker panels are preferably selected toexhibit a hazard ratio of at least about 1.1 or more or about 0.91 orless, more preferably at least about 1.25 or more or about 0.8 or less,still more preferably at least about 1.5 or more or about 0.67 or less,even more preferably at least about 2 or more or about 0.5 or less, andmost preferably at least about 2.5 or more or about 0.4 or less. Theterm “about” in this context refers to +/5% of a given measurement.

The skilled artisan will understand that associating a diagnostic orprognostic indicator, with a diagnosis or with a prognostic risk of afuture clinical outcome is a statistical analysis. For example, a markerlevel of greater than X may signal that a patient is more likely tosuffer from an adverse outcome than patients with a level less than orequal to X, as determined by a level of statistical significance.Additionally, a change in marker concentration from baseline levels maybe reflective of patient prognosis, and the degree of change in markerlevel may be related to the severity of adverse events. Statisticalsignificance is often determined by comparing two or more populations,and determining a confidence interval and/or a p value. See, e.g., Dowdyand Wearden, Statistics for Research, John Wiley & Sons, New York, 1983.Preferred confidence intervals of the invention are 90%, 95%, 97.5%,98%, 99%, 99.5%, 99.9% and 99.99%, while preferred p values are 0.1,0.05, 0.025, 0.02, 0.01, 0.005, 0.001, and 0.0001.

In yet other embodiments, multiple determinations of diagnostic orprognostic markers can be made, and a temporal change in the marker canbe used to determine a diagnosis or prognosis. For example, a markerconcentration in a subject sample may be determined at an initial time,and again at a second time from a second subject sample. In suchembodiments, an increase in the marker from the initial time to thesecond time may be indicative of a particular diagnosis, or a particularprognosis. Likewise, a decrease in the marker from the initial time tothe second time may be indicative of a particular diagnosis, or aparticular prognosis.

The term “sample” as used herein refers to a sample of bodily fluidobtained for the purpose of diagnosis, prognosis, or evaluation of asubject of interest, such as a patient. Preferred test samples includeblood, serum, plasma, cerebrospinal fluid, urine, saliva, sputum, andpleural effusions. In addition, one of skill in the art would realizethat some test samples would be more readily analyzed following afractionation or purification procedure, for example, separation ofwhole blood into serum or plasma components.

Thus, in a preferred embodiment of the invention the sample is selectedfrom the group comprising a blood sample, a serum sample, a plasmasample, a cerebrospinal fluid sample, a saliva sample and a urine sampleor an extract of any of the aforementioned samples. Preferably, thesample is a blood sample, most preferably a serum sample or a plasmasample.

The term “correlating,” as used herein in reference to the use ofdiagnostic and prognostic markers, refers to comparing the presence oramount of the marker(s) in a patient to its presence or amount inpersons known to suffer from, or known to be at risk of, a givencondition; or in persons known to be free of a given condition. Asdiscussed above, a marker level in a patient sample can be compared to alevel known to be associated with a specific diagnosis. The sample'smarker level is said to have been correlated with a diagnosis; that is,the skilled artisan can use the marker level to determine whether thepatient suffers from a specific type diagnosis, and respond accordingly.Alternatively, the sample's marker level can be compared to a markerlevel known to be associated with a good outcome (e.g., the absence ofdisease, etc.). In preferred embodiments, a profile of marker levels arecorrelated to a global probability or a particular outcome.

The invention also pertains to a method for the stratification of apatient into risk groups, said method comprising the steps as describedabove.

The present invention also pertains to the use of any of the abovedescribed assays for predicting the risk of a subject for developingdiabetes mellitus type II.

The invention further relates to the use of an immunoassay for a methodaccording to the invention, wherein at least one anti-copeptin antibodyor fragment or recombinant variant thereof is used to determine thelevel of copeptin or molecular variants thereof in a sample. Preferably,the at least one anti-copeptin antibody is a monoclonal antibody.

Preferably, the immunoassay is a sandwich assay and wherein additionallyto the first anti-copeptin antibody a second anti-copeptin antibody isused to determine the level of copeptin or molecular variants thereof ina sample. Preferably, at least one of said antibodies is a monoclonalantibody.

Also within the scope of the present invention is the in vitro use of acapture probe directed against vasopressin or fragments thereof or itsprecursors or fragments thereof for predicting the risk of an apparentlyhealthy subject for contracting diabetes mellitus and/or metabolicsyndrome or for diagnosing metabolic syndrome in a subject. Preferably,the capture probes are directed against one or more epitopes located inamino acid positions 126-164 of pre-pro-AVP.

The invention also relates to a monoclonal antibody or fragment orrecombinant variant thereof which binds an epitope comprised in apeptide representing positions 132-147 of pre-pro-AVP.

Fragments or recombinant variants of said monoclonal antibody exhibit atleast 80% of the affinity of said monoclonal antibody to pre-pro-AVP.Preferably, fragments or recombinant variants of said monoclonalantibody exhibit at least 80% of the affinity of said monoclonalantibody to the epitope comprised in a peptide representing positions132-147 of pre-pro-AVP. This is a preferred prerequisite for the presentinvention as only the before-mentioned fragments and recombinant variantare as useful as the monoclonal antibody for providing an assayaccording to the present invention which is an assay that has a totalassay imprecision of 20% CV at a concentration below 2.2 pmol/L and alower than 20% CV at normal range-concentrations above thatconcentration, and/or 10% CV at a concentration below 9 pmol/l and alower than 10% CV at normal range-concentrations above thatconcentration. This assay may be provided if at least one of theantibodies is a monoclonal antibody or a fragment or recombinant variantthereof that exhibits at least 80% of the affinity of said monoclonalantibody to the epitope comprised in a peptide representing positions132-147 of pre-pro-AVP.

The invention also relates to the use of a capture probe directedagainst arginine vasopressin pro-hormone or fragments thereof forpredicting the risk of a subject for contracting diabetes mellitusand/or metabolic syndrome or for diagnosing metabolic syndrome.Preferably, the capture probes are directed against one or more epitopeslocated in amino acid positions 126-164 of pre-pro-vasopressin(pre-pro-AVP).

In a further aspect, the present invention relates to the use of any ofthe above described assays or methods in a prophylactic therapy againstdiabetes mellitus, insulin resistance and/or metabolic syndrome.

The amino acid sequence of pre-pro-AVP is given in SEQ ID NO:1 (FIG. 1).The amino acid sequence of copeptin is given in SEQ ID NO:2 (FIG. 2).Copeptin may be glycosylated. Pre-pro-AVP is the precursor peptide forarginine vasopressin pro-hormone (pro-AVP, SEQ ID NO:3, FIG. 3) and itsfragments (including arginine vasopressin, neurophysin II and copeptin).In addition to pro-AVP pre-pro-AVP comprises an N-terminal 19 amino acidsignal sequence. Unless otherwise stated all sequence specificationsherein refer to the sequence of pre-pro-AVP.

As mentioned herein, an “assay” or “diagnostic assay” can be of any typeapplied in the field of diagnostics. Such an assay may be based on thebinding of an analyte to be detected to one or more capture probes witha certain affinity. Concerning the interaction between capture moleculesand target molecules or molecules of interest, the affinity constant ispreferably greater than 10⁸ M⁻¹.

In the context of the present invention, “capture molecules” aremolecules which may be used to bind target molecules or molecules ofinterest, i.e. analytes (i.e. in the context of the present inventionthe cardiovascular peptide(s)), from a sample. Capture molecules mustthus be shaped adequately, both spatially and in terms of surfacefeatures, such as surface charge, hydrophobicity, hydrophilicity,presence or absence of lewis donors and/or acceptors, to specificallybind the target molecules or molecules of interest. Hereby, the bindingmay for instance be mediated by ionic, van-der-Waals, pi-pi, sigma-pi,hydrophobic or hydrogen bond interactions or a combination of two ormore of the aforementioned interactions between the capture moleculesand the target molecules or molecules of interest. In the context of thepresent invention, capture molecules may for instance be selected fromthe group comprising a nucleic acid molecule, a carbohydrate molecule, aRNA molecule, a protein, an antibody, a peptide or a glycoprotein.Preferably, the capture molecules are antibodies, including fragmentsthereof with sufficient affinity to a target or molecule of interest,and including recombinant antibodies or recombinant antibody fragments,as well as chemically and/or biochemically modified derivatives of saidantibodies or fragments derived from the variant chain with a length ofat least 12 amino acids thereof.

The preferred detection methods comprise immunoassays in various formatssuch as for instance radioimmunoassay (RIA), chemiluminescence- andfluorescence-immunoassays, Enzyme-linked immunoassays (ELISA),Luminex-based bead arrays, protein microarray assays, and rapid testformats such as for instance immunochromatographic strip tests.

The assays can be homogenous or heterogeneous assays, competitive andnon-competitive sandwich assays. In a particularly preferred embodiment,the assay is in the form of a sandwich assay, which is a non-competitiveimmunoassay, wherein the molecule to be detected and/or quantified isbound to a first antibody and to a second antibody. The first antibodymay be bound to a solid phase, e.g. a bead, a surface of a well or othercontainer, a chip or a strip, and the second antibody is an antibodywhich is labeled, e.g. with a dye, with a radioisotope, or a reactive orcatalytically active moiety. The amount of labeled antibody bound to theanalyte is then measured by an appropriate method. The generalcomposition and procedures involved with “sandwich assays” arewell-established and known to the skilled person (The ImmunoassayHandbook, Ed. David Wild, Elsevier LTD, Oxford; 3rd ed. (May 2005),ISBN-13: 978-0080445267; Hultschig C et al., Curr Opin Chem. Biol. 2006February; 10(1):4-10. PMID: 16376134, incorporated herein by reference).

In a particularly preferred embodiment the assay comprises two capturemolecules, preferably antibodies which are both present as dispersionsin a liquid reaction mixture, wherein a first labelling component isattached to the first capture molecule, wherein said first labellingcomponent is part of a labelling system based on fluorescence- orchemiluminescence-quenching or amplification, and a second labellingcomponent of said marking system is attached to the second capturemolecule, so that upon binding of both capture molecules to the analytea measurable signal is generated that allows for the detection of theformed sandwich complexes in the solution comprising the sample.

Even more preferred, said labelling system comprises rare earthcryptates or rare earth chelates in combination with a fluorescence dyeor chemiluminescence dye, in particular a dye of the cyanine type.

In the context of the present invention, fluorescence based assayscomprise the use of dyes, which may for instance be selected from thegroup comprising FAM (5- or 6-carboxyfluorescein), VIC, NED,Fluorescein, Fluoresceinisothiocyanate (FITC), IRD-700/800, Cyaninedyes, such as CY3, CY5, CY3.5, CY5.5, Cy7, Xanthen,6-Carboxy-2′,4′,7′,4,7-hexachlorofluorescein (HEX), TET,6-Carboxy-4′,5′-dichloro-2′,7′-dimethodyfluorescein (JOE),N,N,N′,N′-Tetramethyl-6-carboxyrhodamine (TAMRA), 6-Carboxy-X-rhodamine(ROX), 5-Carboxyrhodamine-6G (R6G5), 6-carboxyrhodamine-6G (RG6),RHODAMINE, RHODAMINE GREEN, RHODAMINE RED, RHODAMINE 110, BODIPY dyes,such as BODIPY TMR, OREGON GREEN, Coumarines such as Umbelliferone,Benzimides, such as Hoechst 33258; Phenanthridines, such as TEXAS RED,YAKIMA YELLOW, ALEXA FLUOR, PET, Ethidiumbromide, Acridinium dyes,Carbazol dyes, Phenoxazine dyes, Porphyrine dyes, Polymethin dyes, andthe like.

In the context of the present invention, chemiluminescence based assayscomprise the use of dyes, based on the physical principles described forchemiluminescent materials in Kirk-Othmer, Encyclopedia of chemicaltechnology, 4^(th) ed., executive editor, J. I. Kroschwitz; editor, M.Howe-Grant, John Wiley & Sons, 1993, vol. 15, p. 518-562, incorporatedherein by reference, including citations on pages 551-562. Preferredchemiluminescent dyes are acridiniumesters.

The most preferred embodiment wherein the assay comprises a monoclonaland a polyclonal antibody was compared to an assay comprising twopolyclonal antibodies. To comparatively assess the precision of bothassays, the total assay imprecision was determined by measuring, induplicate, 22 human serum samples with various copeptin concentrations.These data were generated by 6 different operators in 12 assay runs,with 2 different lots of reagents in 2 different laboratories. Theresulting precision profiles are shown in FIGS. 4 and 5. For thepoly/poly assay, the 20% CV was obtained at 2.2 pmol/L and 10% CV at 9pmol/L. In contrast, for the mono/poly assay, the 20% CV was obtained at0.75 pmol/L and 10% CV at 2.9 pmol/L.

The median copeptin concentrations of healthy fasting females at resthas been reported to be 3.2 pmol/L (Bhandari S S, Loke I, Davies J E,Squire I B, Struck J, Ng L L. Gender and renal function influence plasmalevels of copeptin in healthy individuals. Clin Sci (Lond). 2008 Jul.22. [Epub ahead of print]) and 3.7 pmol/L (Morgenthaler N G, Struck J,Alonso C, Bergmann A. Assay for the measurement of copeptin, a stablepeptide derived from the precursor of vasopressin. Clin Chem. 2006January; 52(1):112-9.). In the present analysis the total assayimprecision at 3.2 pmol/L was determined to be 15% for the poly/polyassay, whereas it was 9.5% for the mono/poly assay. Thus, clearly themono/poly assay is much more suitable to assess copeptin levels in thenormal population. Both assays are described in detail below:

Assay 1 (Poly/Poly)

Determination of Copeptin in the chemiluminescence/coated-tube formatwas performed as described (Morgenthaler N G, Struck J, Alonso C,Bergmann A. Assay for the measurement of copeptin, a stable peptidederived from the precursor of vasopressin. Clin Chem. 2006 January;52(1):112-9.). Briefly, the tubes were coated with a purified sheeppolyclonal antibody (2 μg/tube) raised against a peptide representingpositions 132-147 of pre-pro-Vasopressin. A purified sheep polyclonalantibody raised against a peptide representing positions 149-164 ofpre-pro-Vasopressin was labelled withMACN-Akridinium-N-hydroxysuccinimide-Ester and used as tracer. Dilutionsof a peptide representing positions 132-164 of pre-pro-AVP in normalhorse serum served as standards. The immunoassay was performed byincubating 50 μl of samples/standards and 200 μl tracer in coated tubesfor 2 hours at room temperature. The tubes were washed 4 times with 1 mlof LUMItest wash solution (B.R.A.H.M.S. AG, Hennigsdorf Germany), andbound chemiluminescence was measured with an LB952T luminometer(Berthold, Bad Wildbach Germany).

Assay 2 (Mono/Poly)

A monoclonal antibody (294/1A7) was developed, which has an epitope inCopeptin located in the corresponding positions 132-147 ofpre-pro-Vasopressin. The antibody was developed employing standardprocedures as described (Harlow E, Lane D “Antibodies—A LaboratoryManual” 1988 by Cold Spring Harbor Laboratory, pages 148 ff, ISBN0-87969-314-2). Briefly, a chemically synthesized peptide representingpositions 132-164 of pre-pro-Vasopressin and extended by an additionalcystein residue at the N-terminus was coupled to BSA by using Sulfo-MBSas crosslinker. Balb/c mice were immunized and boostered with thisconjugate. Spleen cells of the immunized mice were fused with a SP2/0myeloma cell line, and anti-Copeptin producing hybridoma cells wereselected by their ability to secrete antibodies recognizing a peptidecovering positions 132-147 of pre-pro-Vasopressin. Positively selectedhybridoma cells were recloned, leading to several hybridoma cell lines,one them producing monoclonal antibody 294/1A7, which was further used.

The monoclonal antibody was used to replace the polyclonal antibody usedon the solid phase in poly/poly assay described above. All other assaycomponents and performance of the assay was unchanged as compared to thepoly/poly assay described above.

With the methods and assays of the present invention the risk of asubject to contract diabetes mellitus and/or metabolic syndrome can bedetermined. In other cases a diagnosis of metabolic syndrome, preferablyin combination with other clinical and/or laboratory parameters, in asubject may be performed. From the results of the methods and assays ofthe present invention, a suitable treatment or prevention strategy maybe chosen for a subject. Subjects with increased risk for contractingmetabolic syndrome and/or diabetes mellitus are advised to change theirlifestyles, e.g. to moderately decrease their daily caloric intake,change the dietary composition and/or increase the physical activities.

The correlation between the level arginine vasopressin pro-hormone orfragments thereof in samples of subjects with their risk to contractdiabetes mellitus and/or metabolic syndrome as found by the presentinventors, suggests a role of the vasopressin system in glucosehomeostasis and diabetes development. Increased levels of vasopressin(and its pro-hormone and fragments thereof) are associated with diabetesmellitus and metabolic syndrome. The effect of vasopressin mayparticularly be mediated through the V1b receptor (V1bR). Thus, thepresent invention also pertains to the treatment or prevention ofmetabolic syndrome and/or diabetes mellitus by inhibiting the V1bRreceptor. The present invention relates to V1bR antagonists for the usein the treatment and/or prevention of metabolic syndrome and/or diabetesmellitus. Particularly preferred V1bR antagonists may be selected fromthe group comprising antibodies and small molecules. Possible V1bRantagonists may be identified in binding assays known to a skilledperson by identifying their IC₅₀ values, e.g. using a radio receptorassay.

DESCRIPTION OF DRAWINGS

FIG. 1: Amino acid sequence of pre-pro-AVP (SEQ ID NO:1).

FIG. 2: Amino acid sequence of CT-pro-AVP (copeptin) (SEQ ID NO:2).

FIG. 3: Amino acid sequence of pro-AVP (SEQ ID NO:3).

FIG. 4: Precision profile for the polyclonal/polyclonal antibodycopeptin assay. Inter-assay Coefficient of Variation (CV [%]) plottedagainst copeptin concentrations [pmol/L]. Copeptin concentrations, whichare determined with a CV of 10% and 20%, respectively, are indicated.

FIG. 5: Precision profile for the monoclonal/polyclonal antibodycopeptin assay. Inter-assay CV [%] plotted against copeptinconcentrations [pmol/L]. Copeptin concentrations, which are determinedwith a CV of 10% and 20%, respectively, are indicated.

FIG. 6: Elevated plasma copeptin is independently associated withdiabetes. Plotted are ORs for diabetes for the quartiles of copeptinlevels. Adjusted for: age, sex, triglyceride HDL, Systolic bloodpressure (SBP), diastolic blood pressure (DBP), anti-hypertensivetreatment (AHT), body mass index (BMI), waist, waist-hip-ratio (W/H),cystatin C, CRP, previous cardiovascular disease (CVD).

FIG. 7: Elevated plasma copeptin is an independent predictor of futurediabetes (subjects with known diabetes or fasting blood glucose(FBG)>6.0 mM at baseline excluded). Effect is independent of baselineglucose. Plotted are ORs for incident diabetes for the quartiles ofcopeptin levels. Adjusted for: age, sex, TG, HDL, SBP, DBP, AHT, BMI,waist, W/H, cystatin C, CRP, previous CVD, smoking, diabetes heredity,fP-insulin and fB-glucose.

FIG. 8: Elevated plasma copeptin is an independent predictor of futurediabetes (subjects with known diabetes or FBG≧5.4 mM at baselineexcluded). Effect independent of glucose and remains (strengthens afterexclusion of IFG. Plotted are ORs for incident diabetes for thequartiles of copeptin levels. Adjusted for: age, sex, TG, HDL, SBP, DBP,AHT, BMI, waist, W/H, cystatin C, CRP, previous CVD, smoking, diabetesheredity, fP-insulin and fB-glucose.

FIG. 9: ROC plots for the prediction of future diabetes using a) acombination of age, sex, BMI and diabetes mellitus (DM) heredity and b)by adding copeptin to this combination.

FIG. 10: ROC plots for the prediction of future diabetes using a) acombination of age, sex, SBP, BMI, waist, glucose, TG, HDL and DMheredity and b) by adding copeptin to this combination.

FIG. 11: Fasting blood glucose level (mmol/L) correlated to quartiles ofincreasing plasma copeptin, expressed as mean with 95% confidenceintervals, in non diabetic subjects (n=4377). P (linear trend)<0.001

FIG. 12: Fasting insulin glucose level (mU/L) correlated to quartiles ofincreasing plasma copeptin, expressed as mean with 95% confidenceintervals, in non diabetic subjects (n=4377). P (linear trend)<0.001

EXAMPLES Example 1 Clinical Study

The Malmo Diet and Cancer study (MDC) is a population-based prospectivecohort consisting of 28,449 persons surveyed in 1991-1996 From thiscohort, 6103 persons were randomly selected to be studied for theepidemiology of carotid artery disease, referred to as the MDCcardiovascular cohort (MDC-CC). Fasting plasma samples were obtained in5405 subjects in the MDC-CC. Of those, complete data on covariates,including known risk factors for diabetes, potential confounders andcopeptin in plasma (P-copeptin), was available in 4742 individuals(Table 1).

Diabetes at the baseline exam was defined as self report of a physiciandiagnosis or use of anti-diabetic medication or fasting blood glucose(FBG) of ≧6.1 mmol/L. All analyses in plasma and blood were performed inover-night fasting samples. FBG was measured in whole blood by ahexokinase-glucose-6-phosphate dehydrogenase method. LDL cholesterol wascalculated according to Friedewald's formula. In fasting plasma sampleswhich had been stored at −80° C., we measured copeptin using Assay 2(mono/poly) as described above. C-reactive protein (CRP) was measured bya high-sensitivity assay (Tina-quant CRP, Roche Diagnostics, Basel,Switzerland). Cystatin C was measured using a particle-enhancedimmuno-nephelometric assay (N Latex Cystatin C, Dade Behring, Ill.).

Blood pressure was measured using a mercury-column sphyngomanometerafter 10 minutes of rest in the supine position. Cigarette smoking waselicited by a self-administered questionnaire, with current cigarettesmoking defined as any smoking within the past year. Previouscardiovascular disease was defined as presence of myocardial infarctionor stroke prior to the baseline exam. Diabetes heredity was elicited bya questionnaire and defined as known diabetes in at least one firstdegree relative.

New onset diabetes after the baseline exam until June 2007 was assessedby three registers: (1) The HbA1c-register of Malmo (MHR) at theDepartment of Clinical Chemistry, Malmo University Hospital, whichanalysed and catalogued all HbA1c samples taken in institutional andnon-institutional care in the great Malmo area from 1990 and onwards. Inthe MHR, individuals were defined as having diabetes if they had atleast two HbA1c recordings ≧6.0% using the Swedish Mono-Sstandardization system, which corresponds to 7.0% according to the USNational Glycohemoglobin Standardization Program (NGSP) or at leastthree HbA1c recordings ≧5.5% (corresponding to 6.5% according to NGSP).(2) The Swedish National Diabetes Register (NDR), which was initiated in1996 and covered approximately 50% of all diabetes patients in Sweden in2007; (3) A regional register of the Scania Region in which Malmo is themain city (Diabetes 2000 registry) (Lindholm E, Eur J Epidemiol2001:17:983-989), which was initiated in 2000 and covered approximately25% of all diabetes patients in the Scania region in 2001.

Subjects who did not appear in any of the three registers before thebaseline exam and were free from diabetes at the baseline exam in theMDC-CC according to our criteria (i.e. no self reported history ofphysician diagnosed diabetes, absence of anti-diabetic therapy, FBG atthe baseline exam of <6.0 mmol/L) and were registered as diabetespatients in the NDR or Diabetes 2000 registry or fulfilled our HbA1cbased criteria for diabetes in the MHR anytime after their baseline examin the MDC-CC until June 2007 were classified as having new onsetdiabetes.

The study protocols were approved by the ethics committee of LundUniversity. All participants provided written informed consent.

Statistics

SPSS statistical software (version 14.0) was used for all calculations.Group wise differences in continuous variables were tested usingstudent's t-test and reported as means±SD if normally distributedwhereas they were tested with Mann-Whitney test and reported as mediansand interquartile ranges if not normally distributed. Differences indichotomous variables were tested using chi-2 test. The P-value forlinear trend of fasting glucose and insulin over quartiles P-copeptin innon-diabetic subjects was assessed using linear regression with fastinginsulin concentration transformed with the natural logarithm. We usedcrude and multivariate adjusted logistic regression to test ifincreasing quartiles of P-copeptin (Q1copeptin-Q4copeptin, Q1copeptinbeing reference category) were related to diabetes at baseline in theentire cohort (n=4742). In subjects without diabetes at baseline(n=4377), crude and multivariate logistic regression was used to test ifincreasing quartiles of P-copeptin (Q1copeptin-Q4copeptin, Q1copeptinbeing reference category) were related to insulin resistance, defined asbelonging to the top quartile of fasting insulin, and to new onsetdiabetes. Finally, the relationship between increasing quartiles ofP-copeptin and risk of new onset diabetes was tested in subjects withoutimpaired fasting glucose (fasting plasma glucose 6.1 mmol/Lcorresponding to FBG<5.5 mmol/L) (n=3702). Data from logistic regressionanalyses were expressed as odds ratio (OR) and 95% confidence intervals(CI). A two sided P-value of <0.05 was considered statisticallysignificant.

To assess sensitivity and specificity of P-copeptin in predicting newonset diabetes on top of sets of classical diabetes predictors, wecompared the area under the Receiver Operating Characteristic (ROC)curves using both a personal model (age, gender, BMI and diabetesheredity) and a clinical model (personal model+systolic blood pressure,triglycerides, HDL, waist circumference and FBG) with and withoutP-copeptin in each of the two models.

Results

Of patients with diabetes at the baseline exam (n=365), only 29%reported a history of physician diagnosed diabetes or treatment whereasthe majority (71%) were classified as having diabetes based on FBG≧6.1mmol/L at the baseline exam. P-copeptin was higher among patients withdiabetes as compared to non-diabetic subjects (Table 1). In thenon-diabetic segment of the population, FBG increased with copeptin(FIG. 11). There was a gradual increase in the OR for diabetes withincreasing quartiles of copeptin in both a crude model and afteradjustment for all baseline characteristics which significantly differedin univariate comparisons between diabetes patients and controls(Table 1) except for fasting plasma insulin concentration (model 1adjustment) (Table 2) (FIG. 6). Among non-diabetic subjects, plasmaconcentration of insulin increased gradually with quartiles ofP-copeptin (FIG. 12) and the OR for insulin resistance (top quartile offasting plasma insulin in the non-diabetic segment of the population)increased with P-copeptin in both crude analysis and after extendedmodel 1 adjustment including model 1 covariates and FBG (Table 2). Amongsubjects without diabetes at baseline the copeptin concentrations at thequartile borders were as follows: Q1/Q2: 3.13 pmol/L, Q2/Q3: 5.05pmol/L, Q3/Q4: 7.94 pmol/L.

Among subjects without diabetes at baseline (n=4377), 174 subjectsdeveloped new onset diabetes and among subjects free from impairedfasting glucose at baseline (n=3702), 79 subjects developed new onsetdiabetes during follow-up (Table 3). P-copeptin at baseline wassignificantly higher in subjects who developed new onset diabetescompared with those who did not both among subjects without diabetes atbaseline and among subjects without impaired fasting glucose at baseline(Table 3). The likelihood of developing new onset diabetes increasedwith P-copeptin quartiles in crude analysis as well as after adjustmentfor model 1 covariates and fasting insulin, FBG, smoking, diabetesheredity and LDL both in subjects without diabetes (FIG. 7) and insubjects without impaired fasting glucose at baseline (FIG. 8) (Table4). In subjects without diabetes at baseline, the area under theReceiver Operating Characteristic (ROC) curve for the prediction offuture diabetes increased from 69.4 to 71.0% (P=0.08) and from 83.2 to84.1% (P=0.007) when copeptin was added to the personal model andclinical model of diabetes prediction, respectively. In subjects withoutimpaired fasting glucose at baseline, the area under the ROC curve forthe prediction of future diabetes increased from 66.3 to 71.3% (P=0.03)(FIG. 9) and from 78.3 to 80.5% (P=0.04) (FIG. 10) when copeptin wasadded to the personal model and clinical model of diabetes prediction,respectively.

Discussion

Here we demonstrate in a large population based cohort that P-copeptinis markedly elevated in diabetes patients and that P-copeptin increaseslinearly with FBG in non-diabetic subjects, i.e. in subjects with FBGlevels that definitely do not affect osmolality. In non-diabeticsubjects there was a strong cross sectional relationship betweenP-copeptin and insulin resistance (as estimated by fasting plasmainsulin) and this relationship was independent of a large number ofconfounding factors, including FBG. Moreover, P-copeptin at baseline wasa strong risk factor for development of new onset diabetes duringfollow-up. Importantly, this relationship was independent of a broadrange of well known risk factors for diabetes at baseline, including FBGand fasting insulin, the latter two of which were strongly crosssectionally related to P-copeptin. When the cohort was restricted tosubjects without impaired fasting glucose the relationship betweenP-copeptin and new onset diabetes was strengthened despite the factalmost half of the new onset diabetes cases occurred among subjects whohad impaired fasting glucose at baseline.

As expected, FBG was the strongest risk factor for new onset diabetes.Each 1 mmol/L increase of FBG at baseline increased the risk of futurediabetes with an OR of 11.4 (95% CI 7.4-17.5) in the fully adjustedmodel (Table 4). Baring this in mind, it is impressive that the topversus bottom quartile of copeptin was associated with an OR of 3.6 fornew onset diabetes in subjects without impaired fasting glucose afterfull adjustment of baseline covariates including FBG (Table 4).Importantly, in subjects without impaired fasting glucose, there was asignificant 2.2-4.0% improvement of the area under the ROC curve whenP-copeptin was added to models of classical diabetes risk factors,indicating that P-copeptin improves sensitivity and specificity ofindividual risk prediction of diabetes. In the larger sample, includingsubjects with impaired fasting glucose, P-copeptin improved the areaunder the ROC curve less markedly. The lesser improvement of the areaunder the ROC curve in analyses including subjects with higher levels ofFBG (i.e. subjects with impaired fasting glucose) may result from FBGbeing a more powerful predictor of diabetes at FBG levels near thediagnostic limit of 6.1 mmol/L, whereas markers that are not diagnosticfor diabetes, such as P-copeptin, may better signal diabetessusceptibility earlier in the pre-diabetic state. Importantly, novelrisk markers as screening tools for future diabetes risk are moreimportant in subjects with normal FBG as subjects with impaired fastingglucose commonly get medical attention aimed at predicting diabetesanyway, given the well known strong diabetes predictive value of anelevated FBG. Thus, our findings indicate that there may be clinicalvalue in adding P-copeptin to established screening tools for predictionof diabetes. We do not have data on subtypes of new onset diabetes,however, given the mean age of 57 years in the non-diabetic segment ofour population and the strong cross sectional relationship betweenP-copeptin and insulin resistance, we assume that elevation ofP-copeptin indicates risk of type 2 diabetes rather than type 1diabetes.

Apart from having implications for diabetes prediction, our findingssuggest a role of the AVP system in the pathophysiology of diabetes andpotentially for development of novel antidiabetic treatment regimens.Animal studies have shown that mice lacking the V1aR (V1aR −/−) displayelevated levels of AVP, glucose intolerance and insulin resistancewhereas mice lacking the V1bR (V1bR −/−) get the opposite phenotype oflower FBG and improved insulin sensitivity. Based on these animal dataand our own findings that elevated P-copeptin is associated withelevated FBG, insulin resistance and increased risk of future diabetes,it can be speculated that elevated AVP, as a consequence of AVPresistance at the level of the V1aR or elsewhere, contributes to insulinresistance and diabetes through stimulation of the V1bR. In fact,pharmacological blockade of the V2R, a potent stimulus of increased AVPsecretion through negative feedback on the hypothalamic-neuropituitaryaxis, was associated with a five-fold increase in hyperglycaemic eventsduring 30 days of tolvaptane treatment in patients with hyponatremia.Thus elevation in AVP, whatever the underlying mechanism is, maycontribute to impaired glucose homeostasis, possibly through stimulationof the V1bR. These findings warrant studies on pharmacologicalmanipulations of the AVP system in relation to glucose metabolism inman.

The number of new onset diabetes cases in the MDC-CC may seem lower thanexpected. There are three important reasons which are the most likely toexplain this. (1) The participation rate in the MDC study was only 40%and as a consequence of that, the MDC population is healthier andincludes disproportionally many women compared to the backgroundpopulation (Manjer 2001). As a result of this, the relationship betweenP-copeptin and new onset diabetes that we describe are likely to beunderestimated. (2) The diabetes incidence we observe in the threeregisters are based on the prerequisite that people actively seek healthcare and not on population screening of glucose levels, leading to lowerincidence than observed in studies regularly screening for diabetes bymeasuring FBG. Furthermore, in contrast to our definition of new onsetdiabetes, the definition of diabetes at the baseline exam of MDC-CCincluded measurement of FBG. In fact, of those who were defined ashaving diabetes at 71% did not report a history of diabetes orantidiabetic treatment but were diagnosed solely based on FBG. Exclusionof this large number of diabetes patients, who were unaware that theyhad diabetes before the baseline exam, has further markedly decreasedthe incidence of new onset diabetes during follow-up. (3) Despite theissues discussed above, our study is likely to have missed a number ofnew onset cases who were in fact diagnosed with diabetes within thehealth care system. The MHR recorded all HbA1c values in the greaterMalmo area from 1990 and onwards, however, we missed cases diagnosedwith diabetes in the greater Malmo area without HbA1c being measured orif HbA1c was only measured once or if it was only marginally elevated onrepeated occasions. New onset diabetes cases not detected in the MHR asa result of moving outside the greater Malmo area after the MDC-CCbaseline exam, would have a reasonable chance to be detected by thenation wide NDR and/or the regional Diabetes 2000 registry, inparticular by the NDR as this register was estimated to cover 50% of alldiabetes patients in Sweden in 2007. Still, the coverage of the NDR andDiabetes 2000 registers is incomplete. For these reasons, we haveclassified an unknown number of subjects who did in fact developdiabetes during follow-up as non-diabetics. On the other hand, given thestrict HbA1c criteria for new onset diabetes in the MHR and the provenvalidity of the NDR and Diabetes 2000 register, we are confident on thevalidity of the endpoint in those subjects whom we did classify as newonset diabetes cases. The validity of the register based new onsetdiabetes diagnosis is further supported by the fact that most of thewell-known risk factors for diabetes were markedly elevated at theMDC-CC baseline in these subjects as compared to those who did notdevelop diabetes according to the three registers (Table 3).Importantly, these differences were equally pronounced when the studypopulation was restricted to subjects without impaired fasting glucoseat baseline excluding the possibility that our findings regardingestablished diabetes risk factors and P-copeptin in relation to newonset diabetes were solely driven by subjects who had almost diabeticFBG at the MDC-CC baseline exam.

In conclusion, P-copeptin predicts diabetes independently of a broadrange of established diabetes risk factors, including fasting levels ofglucose and insulin. Our findings suggest a role of the AVP system indiabetes development and may have implications for risk assessment andnovel anti-diabetic pharmacotherapy.

TABLE 1 Baseline characteristics of subjects with and without diabetesNon-diabetic Diabetes subjects patients (n = 4377) (n = 365) P-value Age(years) 57.4 ± 5.9  59.5 ± 5.5  <0.001 FBG (mmol/l)  4.9 ± 0.45 8.1 ±3.0 <0.001 Triglycerides  1.12 (0.85-1.53)  1.64 (1.13-2.33) <0.001(mmol/l)* Systolic BP 140 ± 19  150 ± 20  <0.001 (mmHg) Diastolic BP  87± 9.4  90 ± 9.5 <0.001 (mmHg) BMI (kg/m2) 25.5 ± 3.7  28.7 ± 4.5  <0.001Waist (cm) 82.6 ± 12   94.4 ± 13   <0.001 Waist-hip-ratio 0.84 ± 0.090.91 ± 0.09 <0.001 HDL (mmol/l) 1.40 ± 0.37 1.23 ± 0.35 <0.001 LDL(mmol/l)  4.2 ± 0.98 4.2 ± 1.0 0.22 Cystatin C (mg/l) 0.773 ± 0.1430.809 ± 0.193 0.001 Copeptin (pmol/l)*  5.04 (3.12-7.94)  6.90(4.32-10.7) <0.001 Insulin (mU/L)* 6.0 (4.0-9.0) 12 (7.0-18) <0.001 CRP(mg/l) 1.3 (0.6-2.6) 2.3 (1.3-4.5) <0.001 Men (%) 39.2 56.2 <0.001 AHT(%) 14.7 37.3 <0.001 Current smoker (%) 26.5 24.4 0.41 Diabetes  3.0 3.0 0.87 heredity (%) Previous CVD (%)  2.0  3.8 0.02 Continuousvariables are given as means ± SD unless otherwise specified *median(interquartile range) AHT, anti-hypertensive treatment; BMI,Body-mass-index; FBG, fasting blood glucose; previous CVD,cardiovascular disease present before baseline examination..

TABLE 2 Prevalent diabetes and insulin resistance in relation toquartiles of baseline P-copeptin. OR (95% CI) OR (95% CI) OR (95% CI) Pfor Q2 copeptin vs Q3 copeptin vs Q4 copeptin vs linear Dependentvariable Q1copeptin Q1 copeptin Q1 copeptin trend Diabetes* crude 1.44(1.00-2.07) 1.92 (1.36-2.71) 2.83 (2.04-3.93) <0.001 Adjusted 1.19(0.81-1.75) 1.39 (0.96-2.01) 1.45 (1.00-2.11) 0.04 Hyper- crude 1.30(1.06-1.60) 1.53 (1.25-1.87) 2.34 (1.93-2.85) <0.001 insulinemia†Adjusted 1.19 (0.94-1.51)2 1.26 (0.99-1.60)2 1.61, 1.26-2.06)2 <0.001*Analysis of diabetes prevalence (n = 365) in the entire cohort (n =4742) †Analysis of hyperinsulinemia (highest quartile of fasting plasmainsulin concentration among non-diabetic subjects) anong non-diabeticsubjects (n = 4377) ‡Adjusted for age, sex, HDL, triglycerides, bloodpressure, antihypertensive treatment, body mass index, waist, waist/hipratio, cystatin C, CRP and prevalent cardiovascular disease (model 1)§Adjusted for model 1 and FBG.

TABLE 3 Baseline characteristics in subjects who did and did not convertto diabetes during follow-up Subjects without diabetes at baseline (n =4377) Non-converters Incident diabetes (n = 4203) (n = 174) P Age(years) 57.3 ± 5.9  57.9 ± 5.7  0.26 Men (%) 38.9 45.4 0.09 Glucose(mmol/l)  4.9 ± 0.44  5.4 ± 0.44 <0.001 Triglycerides  1.11 (0.84-1.51) 1.46 (1.05-1.96) <0.001 (mmol/l) Systolic BP 140 ± 19  146 ± 19  <0.001(mmHg) Diastolic BP  86 ± 9.3 90 ± 10 <0.001 (mmHg) AHT (%) 14.2 27.6<0.001 BMI (kg/m2) 25.4 ± 3.6  28.2 ± 4.7  <0.001 Waist (cm) 82.2 ± 12  91.5 ± 14   <0.001 Waist-hip-ratio 0.84 ± 0.09 0.89 ± 0.10 <0.001 HDL(mmol/l) 1.41 ± 0.37 1.25 ± 0.33 <0.001 LDL (mmol/l)  4.2 ± 0.98 4.3 ±1.0 0.06 Cystatin C (mg/l) 0.77 ± 0.14 0.82 ± 0.21 <0.001 Copeptin(pmol/l)  4.98 (3.09-7.84)  6.35 (4.05-9.88) <0.001 Insulin (mU/L) 6.0(4.0-9.0) 9.0 (6.0-13)  <0.001 CRP (mg/l) 1.2 (0.6-2.6) 2.1 (0.9-4.1)<0.001 Current smoker (%) 26.4 29.5 0.37 Diabetes  2.8  6.9 0.002heredity (%) Previous CVD (%)  2.0  2.3 0.80 Subjects without impairedfasting glucose at baseline (n = 3702) Non-converters Incident diabetes(n = 3623) (n = 79) P Age (years) 57.2 ± 5.9 58.1 ± 5.4 0.20 Men (%)36.5 38.0 0.79 Glucose (mmol/l)  4.7 ± 0.34  5.0 ± 0.28 <0.001Triglycerides  1.34 (0.82-1.47)  1.34 (1.06-1.92) <0.001 (mmol/l)Systolic BP 139 ± 19 146 ± 19 0.001 (mmHg) Diastolic BP   86 ± 9.2   89± 9.5 0.004 (mmHg) AHT (%) 13.4 24.1 0.006 BMI (kg/m2) 25.2 ± 3.6 27.5 ±4.9 <0.001 Waist (cm) 81.3 ± 12  88.7 ± 14  <0.001 Waist-hip-ratio  0.84± 0.09  0.87 ± 0.09 <0.001 HDL (mmol/l)  1.43 ± 0.37  1.27 ± 0.32 <0.001LDL (mmol/l)  4.2 ± 0.98  4.1 ± 0.93 0.99 Cystatin C (mg/l)  0.77 ± 0.14 0.83 ± 0.27 <0.001 Copeptin (pmol/l)  4.90 (3.03-7.65)  6.74(4.44-10.9) 0.001 Insulin (mU/L) 6.0 (4.0-8.0) 8.0 (6.0-11)  <0.001 CRP(mg/l) 1.2 (0.6-2.4) 2.4 (1.0-4.1) <0.001 Current smoker (%) 25.4 30.40.31 Diabetes  3.0  7.6 0.02 heredity (%) Previous CVD (%)  1.9  2.50.67 Continuous variables are given as means ± SD unless otherwisespecified *median (interquartile range) AHT, anti-hypertensivetreatment; BMI, Body-mass-index; FBG, fasting blood glucose; previousCVD, cardiovascular disease present before baseline examination..

TABLE 4 New onset diabetes in relation to quartiles of baselineP-copeptin. OR (95% CI) OR (95% CI) OR (95% CI) Q2 copeptin vs Q3copeptin vs Q4 copeptin vs P (test for Dependent variable Q1 copeptin Q1copeptin Q1 copeptin linear trend) Incident crude 1.28 (0.76-2.15) 1.94(1.19-3.14) 2.64 (1.66-4.19) <0.001 diabetes among adjusted‡ 1.37(0.78-2.39) 1.79 (1.06-3.05) 2.09 (1.23-3.56) 0.004 non-DM* Incidentcrude 1.85 (0.81-4.20) 2.12 (0.95-4.73) 4.56 (2.18-9.52) <0.001 diabetesamong adjusted‡ 1.80 (0.78-4.16) 1.92 (0.84-4.38) 3.48 (1.58-7.65) 0.001non-IFG† *Subjects who developed diabetes during follow-up (n = 174)among subjects without diabetes at baseline (n = 4377) †Subjects whodeveloped diabetes during follow-up (n = 79) among subjects withoutimpaired fasting glucose at baseline (n = 3702) ‡Adjusted for age, sex,HDL, triglycerides, blood pressure, antihypertensive treatment, bodymass index, waist, waist/hip ratio, cystatin C, CRP and prevalentcardiovascular disease, smoking, diabetes heredity, LDL, FBG and fastinginsulin.

The invention claimed is:
 1. A method for predicting the risk of asubject for contracting diabetes mellitus and/or metabolic syndrome orfor identifying a subject having an enhanced risk for contractingdiabetes mellitus and/or metabolic syndrome or for diagnosing metabolicsyndrome in a subject wherein said subject is non-diabetic, comprising:(a) detecting and quantitating the level of a marker consisting ofarginine vasopressin pro-hormone or fragments thereof in a sample fromsaid patient, wherein said detection and quantitation comprisescontacting the sample with a diagnostic assay reagent comprising acapture probe that specifically binds to arginine vasopressinpro-hormone or a fragment thereof selected from copeptin and neurophysinII, and detecting and quantitating thus-formed complexes of captureprobe and arginine vasopressin pro-hormone, copeptin or neuorphysin II,and (b) predicting the risk of the subject for contracting diabetesmellitus and/or metabolic syndrome or inferring from it a risk forcontracting diabetes mellitus and/or metabolic syndrome for said subjector for diagnosing metabolic syndrome in said subject by comparing thelevel of arginine vasopressin pro-hormone, copeptin or neurophysin II inthe patient to the level of arginine vasopressin pro-hormone, copeptinor neurophysin II in an ensemble of pre-determined samples in apopulation of comparable apparently healthy subjects who latercontracted diabetes mellitus or metabolic syndrome or did not contractsaid conditions, wherein said prediction or inference is based on astatistically significant correlation of the level of the marker in thepatient sample with the levels of the marker in the pre-determinedsamples.
 2. The method of claim 1, wherein said subject is apparentlyhealthy.
 3. The method of claim 1, wherein said subject is anon-diabetic with fasting blood glucose of less than 6.1 mmol/L but morethan 5.4 mmol/L.
 4. The method of claim 1, wherein said subject is asubject with fasting blood glucose of less than 5.4 mmol/L.
 5. Themethod of claim 1, wherein the risk of the subject for developingdiabetes mellitus type II is determined.
 6. The method of claim 1,wherein the prediction of the risk of the subject for contractingdiabetes mellitus and/or metabolic syndrome or the diagnosis ofmetabolic syndrome is improved by additionally (c) detecting andquantitating the level of at least one laboratory parameter or furthermarker selected from the group consisting of fasting blood or plasmaglucose, triglycerides, high density lipoprotein (HDL) cholesterol orsubfractions thereof, low density lipoprotein (LDL) cholesterol orsubfractions thereof, Cystatin C, Insulin, C-reactive protein (CRP),natriuretic peptides of the A- and the B-type or a precursor or fragmentthereof selected from type A natriuretic peptide (ANP), proANP,N-terminal-proANP (NT-proANP), midregional-proANP (MR-proANP), type Bnatriuretic peptide (BNP), proBNP or N-terminal-proBNP (NT-proBNP),growth differentiation factor 15 (GDF15), interleukin 1 receptor-like 1(ST2), procalcitonin, pro-Adrenomedullin and fragments thereof selectedfrom adreneomedullin (ADM), Proadrenomedullin N-terminal 20 peptide(PAMP), mid-regional-proADM (MR-proADM) or C-terminal proADM(CT-proADM), and pro-Endothelin-1 (proET-1) and fragments thereofselected from C-terminal-proET-1 (CT-proET-1), N-terminal-proET-1(NT-proET-1), big-Endothelin-1 or Endothelin-1; wherein said detectionand quantitation of the further markers selected from the groupconsisting of natriuretic peptides of the A- and the B-type or aprecursor or fragment thereof, GDF15, ST2, procalcitonin and fragmentsthereof, pro-Adrenomedullin and fragments thereof, and pro-Endothelin-1and fragments thereof, comprises contacting the sample with a diagnosticassay reagent comprising a capture probe that specifically binds to saidfurther marker or a precursor or fragment thereof, and detecting andquantitating thus-formed complexes of capture probe and further markeror precursor or fragment thereof, and (d) improving the prediction ofthe risk of the subject for contracting diabetes mellitus and/ormetabolic syndrome or inferring from it a risk for contracting diabetesmellitus and/or metabolic syndrome for said subject or for diagnosingmetabolic syndrome in said subject by comparing the level of each ofsaid laboratory parameters or markers in the patient to the level ofeach of said laboratory parameters or markers in an ensemble ofpre-determined samples in a population of comparable apparently healthysubjects who later contracted diabetes mellitus or metabolic syndrome ordid not contract said conditions, wherein said prediction or inferenceis based on a statistically significant correlation of the levels of thelaboratory parameters or markers in the patient sample with the levelsof the laboratory parameters or markers in the pre-determined samples.7. The method of claim 1, wherein additionally at least one clinicalparameter is determined selected from the group consisting of age,gender, systolic blood pressure, diastolic blood pressure,antihypertensive treatment (AHT), body mass index, waist circumference,waist-hip-ratio, current smoker, diabetes heredity and previouscardiovascular disease (CVD).
 8. The method of claim 1, wherein thesample is selected from the group comprising blood sample, a serumsample, a plasma sample, a cerebrospinal fluid sample, a saliva sampleand a urine sample or an extract of any of the aforementioned samples.9. The method of claim 1, wherein the level of the fragment of argininevasopressin copeptin is detected and quantitated.
 10. The method ofclaim 1, wherein the detection and quantitation comprises performing animmunoassay, wherein at least one anti-copeptin antibody or fragment orrecombinant variant thereof is used to determine the level of thecopeptin fragment of arginine vasopressin or molecular variants thereofin a sample.
 11. The method of claim 10, wherein the immunoassay is asandwich assay and wherein a second antibody is used to determine thelevel of copeptin or molecular variants thereof in a sample.
 12. Themethod of claim 1, wherein a two sided P-value of <0.05 is consideredstatistically significant.